{"id":"W2067094919","doi":"10.1109/btas.2012.6374570","title":"Securing handheld devices and fingerprint readers with ECG biometrics","year":2012,"lang":"en","type":"article","venue":"","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biometrics; Mobile device; Fingerprint (computing); Computer science; Flexibility (engineering); Fingerprint recognition; Linear discriminant analysis; Artificial intelligence; SIGNAL (programming language); Pattern recognition (psychology); Computer vision; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001418739,0.00007451625,0.0001367398,0.0002405856,0.00005052041,0.00001915348,0.00002213137,0.00003577199,0.00004142168],"category_scores_gemma":[0.00003660125,0.00004787705,0.00002393377,0.0004470309,0.00002880131,0.00007626828,0.00002061788,0.00007588611,0.00001621501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001634828,"about_ca_system_score_gemma":0.000009260173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00013428,"about_ca_topic_score_gemma":0.00000984914,"domain_scores_codex":[0.9994835,0.000006438717,0.00008158172,0.0001068629,0.0001344668,0.0001871015],"domain_scores_gemma":[0.999621,0.00004705017,0.0000270404,0.0001170278,0.00003092449,0.0001569747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001455408,0.00004745336,0.9732815,0.00007667916,0.00009883258,0.000004723642,0.0004612093,9.266413e-7,0.000631989,0.00001965879,0.00007036697,0.02529208],"study_design_scores_gemma":[0.002687073,0.0006608157,0.8687252,0.0007507,0.001367482,0.0002276096,0.01102379,0.003794334,0.08966916,0.00001914495,0.0203226,0.0007520376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900609,0.00124498,0.002775316,0.0004463956,0.00005321925,0.00004258072,3.313896e-7,0.00006351726,0.005312739],"genre_scores_gemma":[0.9861389,0.00008878803,0.0120381,0.0001181182,0.0001807514,0.000002305112,0.000001646982,0.000008801978,0.001422573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1045563,"threshold_uncertainty_score":0.1952369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0260857406647147,"score_gpt":0.2767792392046402,"score_spread":0.2506934985399255,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}